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Evaluating Qualitative Expectational Data on Investments from Business Surveys

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Abstract

This paper assesses the properties of qualitative expectations on investment collected through business sample surveys conducted by the Bank of Italy. Non-parametric tests for the rationality of qualitative data are verified under three scenarios, namely in the case firms report their qualitative expectation as the mode, the median, or the mean of their subjective density. Under the first two scenarios, expectations result mostly rational, while, under the scenario assuming that firms have in mind the mean of their subjective density, rationality is not satisfied with the thresholds of response options defined in the qualitative questions. However, qualitative expectations result being not just noise but contain some signal of the quantitative outcome data provided by the same sample. Moreover, the analysis reveals that both qualitative forecasts and outcomes are coherent with their corresponding quantitative data.

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Notes

  1. At the beginning the surveys covered only industrial processing firms with at least 50 workers. Over time, the universe of interest of both surveys has gradually expanded to include energy-extraction firms (since SISF on 1999), firms with 20-49 employees (since SISF on 2001), private non-financial services firms with 20 or more employees (since BOS on 2002), the construction sector with 20 or more employees (since 2006), and construction firms with 10 or more employees (since 2013).

  2. The specification of the type of investment expenditures to be considered was included since 2000.

  3. The generalized Beta distribution has density:

    $$\begin{aligned} \text{ Beta }(x;a,b,l,r)=\frac{\Gamma (a+b)}{\Gamma (a)\Gamma (b)}\frac{(x-l)^{a-1}(r-x)^{b-1}}{(r-l)^{a+b-1}}, \qquad \text{ for }\quad l \le x \le r, \end{aligned}$$

    where \(\Gamma (a)=\int _{0}^{\infty }x^{a-1}e^{-x}dx\). Since the empirical shapes in the five intervals are very different (in the lower extreme they are not even monotone), I choose this random variable because the two shape parameters a and b give considerable flexibility and the two location parameters l and r allow to specify the support of the distribution (Engelberg et al., 2009).

  4. The left tail, on the other hand, is left raw since it is bounded to \(-100\%\) by construction.

  5. In the last decade, only in one year the p-value is smaller than 5%.

  6. Although questions in the Bank of Italy qualitative business survey define proper thresholds for the response options, I will employ also this weaker test since, as it will be shown, data do not satisfy the best-case scenario requirement with the current thresholds.

  7. In this case I adjust the sample weights by dividing them by the number of times the corresponding company appears in the pooled sample, so as not to inflate the representativeness of repeatedly interviewed observations.

References

  • Bachmann, R., & Elstner, S. (2015). Firm optimism and pessimism. European Economic Review, 79, 297–325.

    Article  Google Scholar 

  • Baltagi, B. (2001). Econometric Analysis of Panel Data. John Wiley & Sons, Hoboken, NJ, 3rd edition.

  • Bergström, R. (1995). The relationship between manufacturing production and different business survey series in Sweden 1968–1992. International Journal of Forecasting, 11(3), 379–393.

    Article  Google Scholar 

  • Botsis, A., Görtz, C., & Sakellaris, P. (2020). Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms’ Forecasts. Working Paper 8148, CESifo Group, Munich.

  • Bottone, M., Modugno, L., & Neri, A. (2022). Response burden and data quality in business surveys. Journal of Official Statistics, 37(4), 811–836.

    Article  Google Scholar 

  • Cassar, G., & Gibson, B. (2007). Forecast rationality in small firms*. Journal of Small Business Management, 45(3), 283–302.

    Article  Google Scholar 

  • Chambers, R., Kokic, P., Smith, P., & Cruddas, M. (2000). Winsorization for identifying and treating outliers in business surveys. In Proceedings of the second international conference on establishment surveys (pp. 717–726). American Statistical Association.

  • Claveria, O., Monte, E., & Torra, S. (2019). Evolutionary computation for macroeconomic forecasting. Computational Economics, 53, 833–849.

    Article  Google Scholar 

  • Cooper, A. C., Woo, C. Y., & Dunkelberg, W. C. (1988). Entrepreneurs’ perceived chances for success. Journal of Business Venturing, 3(2), 97–108.

    Article  Google Scholar 

  • Das, M., Dominitz, J., & van Soest, A. (1999). Comparing predictions and outcomes: Theory and application to income changes. Journal of the American Statistical Association, 94(445), 75–85.

    Article  Google Scholar 

  • D’Aurizio, L. & Papadia, G. (2016). Using auxiliary information to evaluate the estimation bias in a business panel survey. Bank of Italy QEF, (329).

  • Engelberg, J., Manski, C. F., & Williams, J. (2009). Comparing the point predictions and subjective probability distributions of professional forecasters. Journal of Business and Economic Statistics, 27(1), 30–41.

    Article  Google Scholar 

  • Englmaier, F. (2010). Managerial optimism and investment choice. Managerial and Decision Economics, 31(4), 303–310.

    Article  Google Scholar 

  • European Commission (2016). The joint harmonized EU programme of business and consumer surveys. European Union.

  • Ferrando, A., Ganoulis, I., & Preuss, C. (2020). What were they thinking? Firms’ expectations since the financial crisis. Review of Behavioral Finance, 13(4), 370–385.

    Article  Google Scholar 

  • Girardi, A., Gayer, C., & Reuter, A. (2016). The role of survey data in nowcasting euro area gdp growth. Journal of Forecasting, 35(5), 400–418.

    Article  Google Scholar 

  • Gourieroux, C., & Pradel, J. (1986). Direct test of the rational expectation hypothesis. European Economic Review, 30(2), 265–284.

    Article  Google Scholar 

  • Horvath, B., Nerlove, M., & Wilson, D. (1992). A re-interpretation of direct tests of forecast rationality using business survey data. In K. H. Oppenlander & G. Poser (Eds.), Business cycle analysis by means of economic surveys, part 1 (pp. 131–152). Avebury: Aldershot.

  • Ivaldi, M. (1992). Survey evidence on the rationality of expectations. Journal of Applied Econometrics, 7(3), 225–241.

    Article  Google Scholar 

  • Lemmens, A., Croux, C., & Dekimpe, M. G. (2005). On the predictive content of production surveys: A pan-european study. International Journal of Forecasting, 21(2), 363–375.

    Article  Google Scholar 

  • Lui, S., Mitchell, J., & Weale, M. (2011). Qualitative business surveys: signal or noise? Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(2), 327–348.

    Article  Google Scholar 

  • Lui, S., Mitchell, J., & Weale, M. (2011). The utility of expectational data: Firm-level evidence using matched qualitative-quantitative uk surveys. International Journal of Forecasting, 27(4), 1128–1146.

    Article  Google Scholar 

  • Madeira, C. (2018). Testing the rationality of expectations of qualitative outcomes. Journal of Applied Econometrics, 33(6), 837–852.

    Article  Google Scholar 

  • Manski, C. F. (1990). The use of intentions data to predict behavior: A best-case analysis. Journal of the American Statistical Association, 85(412), 934–940.

    Article  Google Scholar 

  • Nerlove, M. (1983). Expectations, plans, and realizations in theory and practice. Econometrica, 51(5), 1251–1279.

    Article  Google Scholar 

  • OECD (2000). Economic Surveys and Data Analysis. CIRET Conference Proceedings, Paris.

  • Pimentel, J. L. (2019). Some biases in likert scaling usage and its correction. International Journal of Science: Basic and Applied Research, 45(1), 181–191.

    Google Scholar 

  • United Nations (2015). Handbook on Economic Tendency Surveys. United Nations.

  • Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39, 806–820.

    Article  Google Scholar 

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Authors and Affiliations

  1. Bank of Italy, Rome, Italy

    Lucia Modugno

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  1. Lucia Modugno
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Correspondence to Lucia Modugno.

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The views expressed are not necessarily shared by the Bank of Italy. No funding was received to assist with the preparation of this manuscript. The author has no relevant financial or non-financial interests to disclose.

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Appendix: Additional Tables and Figures

Appendix: Additional Tables and Figures

Table 5 Number of firms in the BOS original dataset, in the BOS panel subset without missing observations in the two involved questions (absolute and in percentage over the yearly whole BOS sample), in the original SISF dataset and in the matched panel BOS without missing observations and SISF
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Fig. 9
figure 9

Boxplots of the quantitative realized investment change winsorized at 90% and 80%. Source: author’s elaborations on SISF 2002-2020. The winsorization has been performed only to the right tail of the empirical distribution. The left tail is bounded to -100 for construction

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Table 6 Proportion of firms in different employment size classes, sectors and geographic areas
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Table 7 Weighted panel-data multinomial logit model of the nominal variable taking categories “Negatively surprised” if the firm realized less than expected, “Realistic” if the firm spent approximately what expected (taken as a base), and “Positively surprised” if the firm realized more than expected
Full size table

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Modugno, L. Evaluating Qualitative Expectational Data on Investments from Business Surveys. J Bus Cycle Res (2024). https://doi.org/10.1007/s41549-024-00094-8

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